Semantic Communication for Rate-Limited Closed-Loop Distributed Communication-Sensing-Control Systems

TL;DR

This paper introduces a hierarchical semantic communication framework for distributed control systems that optimizes information transmission under bandwidth limits by jointly considering observation, estimation, and control errors.

Contribution

It proposes a unified semantic compression and rate adaptation framework based on Weaver's model, tailored for distributed SCC systems with multiple semantic error levels.

Findings

Effective semantic information capture and task relevance

Dynamic rate allocation improves performance under bandwidth constraints

Hierarchical optimization enhances control and sensing accuracy

Abstract

The growing integration of distributed integrated sensing and communication (ISAC) with closed-loop control in intelligent networks demands efficient information transmission under stringent bandwidth constraints. To address this challenge, this paper proposes a unified framework for goal-oriented semantic communication in distributed SCC systems. Building upon Weaver's three-level model, we establish a hierarchical semantic formulation with three error levels (L1: observation reconstruction, L2: state estimation, and L3: control) to jointly optimize their corresponding objectives. Based on this formulation, we propose a unified goal-oriented semantic compression and rate adaptation framework that is applicable to different semantic error levels and optimization goals across the SCC loop. A rate-limited multi-sensor LQR system is used as a case study to validate the proposed framework. We employ a GRU-based AE for semantic compression and a PPO-based rate adaptation algorithm that dynamically allocates transmission rates across sensors. Results show that the proposed framework effectively captures task-relevant semantics and adapts its resource allocation strategies across different semantic levels, thereby achieving level-specific performance gains under bandwidth constraints.